Your phone is an incredible example of engineering, but it wouldn’t be able to correctly model a single caffeine molecule in interaction with your brain. The issue here isn’t about processing capability – it is about the way you talk. In order to really describe the natural world, a computer needs to be able to communicate in a way that speaks the language of the natural world — that is, using the rules of quantum mechanics.
Classical computers, even the world’s best, face insurmountable complexity when simulating anything beyond particles as their number grows. The difficulty of solving this puzzle grows so fast that the addition of another particle increases its difficulty by a factor of a million. Classical computers have reached a computational limit that prevents them from discovering the perfect medicine or developing revolutionary battery designs. They’re just too large for the tools they have available.
What if, rather than attempting to brute force a solution with 1s and 0s, we were able to create a computer that operates on the principles of quantum mechanics, much in the same way that the natural world operates? A machine that thinks like nature does.
There’s a new approach that combines both quantum simulation and artificial intelligence to destroy the wall. A hybrid computation methodology, capable of being thought of as a computer that “thinks” like nature itself, has the potential to solve seven very specific problems that have long been thought of as unsolvable, problems that can potentially change the world.
Summary
“Quantum AI Simulation: Solving 7 Breakthrough Problems That Classical Computers Can’t Model” illustrates why classical computers have a “complexity wall”: as the number of particles or interactions increases, the amount of computational resources required for simulation grows exponentially. Quantum computers model systems using qubits, which enable the simultaneous representation of multiple states via a process known as superposition.
This enables the simultaneous exploration of numerous potential solutions. However, the vast search space of a quantum computer requires some form of guidance — and that is what Artificial Intelligence provides: it acts as a navigator, identifying patterns, and pointing the quantum simulation toward plausible solutions.
Seven problem areas are highlighted in the article as having the potential to transform using the combination of Quantum and AI:
(1) Developing new drugs much faster than currently possible using quantum simulations of molecular-protein interactions at the atomic level;
(2) Developing improved battery materials that will provide both higher capacities and faster charge times;
(3) Developing catalysts for low-energy fertilizers that mimic the enzyme efficiencies found in nature;
(4) Improving financial risk models by being able to capture multiple interdependent variables;
(5) Developing ultra-selective carbon-capture materials;
(6) Simulating the folding pathways of proteins to develop methods to prevent disease caused by misfolded proteins; and
(7) Optimizing global logistics beyond the current limitations of classical route planning.
The article concludes by noting why this is not currently happening: today’s qubits are unstable and prone to errors, so more stable and accurate qubits need to be developed before wide-scale adoption can occur. However, the author believes that we are now entering an era of “nature-based computing” and expects significant impacts on real-world problems.
Quantum AI simulation: A visual showing glowing Qubits and Neural Networks modeling complex systems beyond Classical Computing
Quantum AI simulation is an emerging field that combines machine learning techniques with quantum computing to simulate large-scale, complex systems. In traditional simulations, when multiple interacting particles are simulated, the memory required to represent these interactions increases exponentially; as a result, even powerful computers have to make simplifications in order to simulate such large numbers of particles. Quantum AI Simulation addresses this problem by representing states with qubits and simulating the probabilities that nature itself employs via quantum circuits.
Quantum AI Simulation is rarely implemented strictly from a “quantum perspective.” Researchers typically implement hybrid approaches in which a quantum processor produces samples or estimates the energy associated with various system configurations, and the AI models then use the data produced by the quantum processor to denoise the output, optimize quantum-circuit parameters, and create compact representations of data. The iterative loop of the hybrid workflow can significantly reduce the number of times the quantum processor needs to be run. This is significant, since current devices are both noisy and expensive to access.
One of the major promises of Quantum AI Simulation is improved chemistry and materials design. Quantum behavior defines reaction pathways, battery performance, superconductivity, and catalysts. Due to the complexity of accurately calculating these behaviors at high levels of accuracy, they soon become computationally intractable with traditional methods. By combining variational quantum algorithms with neural networks, Quantum AI Simulation can efficiently search for low-energy molecular configurations, estimate electronic wavefunctions, and identify promising candidate compounds for experimental evaluation.
Quantum AI Simulation has the potential to aid optimization problems that can be represented as an energy landscape (such as logistics, portfolio balancing, and grid management) by having AI provide good starting points and constraints for the problem, and by having the quantum routine find many of the possible configurations in parallel.
Even if no quantum advantage exists at present, Quantum AI Simulation will enable new heuristics for solving problems more quickly than manual tuning of methods.
Quantum AI Simulation also enables fundamental scientific research. In both particle and condensed-matter physics, simulations of strongly correlated systems are challenging. Quantum AI Simulation enables physicists to simulate simple lattice models and study phase transitions, magnetism, and transport properties with fewer assumptions than other methods.
Finally, Quantum AI Simulation provides a mechanism to improve our ability to manage uncertainty. The outputs from quantum measurements are inherently noisy and probabilistic; AI is able to learn from noisy or “messy” signals and therefore can be used to mitigate errors in the output from a simulation, to detect drift and identify outliers and to estimate confidence intervals around estimates made from multiple runs of a simulation on different quantum computers at different times and under different calibration conditions.
This will make simulation outputs more reliable and trustworthy for decision-making and will allow groups to compare outputs from simulations run on different quantum computers and under different conditions, without requiring a complete rewrite of their workflow as they scale from prototype to pilot-scale operations.
The field remains very young; current limitations to widespread adoption include substantial hardware noise, relatively few qubits available today, and the need for error correction. However, progress in this area is occurring rapidly in algorithm development, benchmarking, and tool development. As the number of qubits in each device increases and as the quality of AI-based controls over those devices increases, Quantum AI Simulation could potentially become a practical “computational microscope”, enabling researchers to see patterns that do not exist in classical models and thus accelerate discovery in many fields.
What If a Computer Could Explore Millions of Possibilities at Once?
Breaking down the computational wall will require a new type of instrument that operates outside traditional parameters. All computers, including the largest supercomputers currently available, process information using a binary system based on absolute values created from bits. A bit is analogous to a light switch; it has an on position (1) or an off position (0). The simplicity of this binary system allows computers to evaluate a vast number of solutions to complex problems one after another, as if testing many different keys in a lock.
In contrast, quantum computers utilize a fundamental unit called a qubit. In terms of how it functions, instead of simply being an on/off switch (as a light switch is), a qubit is more akin to a dimmer switch. A qubit can exist in three states: an off state (0), an on state (1), or it can also exist in a state that represents a blend of both states at the same time. This ability for a qubit to exist in multiple states at the same time is due to a property of quantum systems called superposition. Superposition is a fundamental property of quantum systems and is what provides the basis for the incredible speed and power of quantum computers.
At this point, everything changes. As a result of the property of superposition, a quantum computer does not have to test each possibility in sequence. Rather, a small group of qubits can test an enormous number of possible solutions simultaneously. For example, with 300 qubits working together, the number of possible combinations that can be evaluated is greater than the number of atoms that make up our observable universe. Quantum computers do not simply find the correct solution much quicker than classical computers; rather, they are able to test all possible solutions simultaneously.
Finding the Needle in a Universe-Sized Haystack: The Role of AI
The ability to store information in one place, with room for many more possibilities than there are atoms in the entire universe, is both a benefit and a detriment. There will always be the correct answer somewhere, but finding it is an issue. In this respect, the amount of information is like a map showing every single route through a mountain range – an incredible mass of information. You don’t need additional information; you need someone to understand how to read the map.
That is when AI (Artificial Intelligence) has the potential to become the ultimate map reader to navigate the world of quantum physics. In contrast to allowing the quantum computer to randomly explore its simulation, AI can monitor the simulation, identify subtle patterns, and learn as it proceeds. It will tell the quantum system, “In that general area, I see something interesting, let’s investigate over there.” This is equivalent to identifying a vast number of paths that would never lead anywhere and eliminating them without actually taking any of them.
While the quantum computer provides a massive, parallel “what if” engine, the AI provides the strategic approach to efficiently guide the search. Together, they turn the impossible task of searching for the needle in the haystack of the universe into a simple and targeted search for the needle. This powerful combination of the two is precisely what is needed to solve problems that were previously considered insurmountable.
Quantum Systems: Quantum systems represent reality through probabilities and interactions
Quantum systems are models of reality that may be foreign to many people’s experiences; they provide a picture of what could occur for each event, described by its likelihood. Quantum systems have a wide variety of unique features that distinguish them from other types of systems such as the ability for particles to behave as waves, the capability for these particles to exist in multiple states simultaneously (a property known as superposition) and the ability for particles to become “entangled” with one another (so that the characteristics of two particles will remain connected regardless of the distance between them).
Quantum systems are not random in the sense that they do not produce results based on chance alone; they operate according to rules and are governed by an underlying probabilistic structure that is consistent with experimental evidence.
The interactions in quantum systems are another distinguishing feature. These interactions are represented mathematically by state vectors (or wave functions) and evolve as new interactions occur. The act of measuring a quantum system creates yet another element of difference. The interaction with a quantum system during measurement selects a specific outcome from the set of possibilities associated with that system, and the selection depends on how the measurement is performed.
As the number of interacting quantum systems increases, electrons in materials, atoms in molecules, spins in magnets, etc., the number of possible configurations of these systems increases exponentially, creating a significant challenge to accurately predicting the behavior of these systems.
As researchers begin to model large-scale quantum systems, there is growing interest in quantum artificial intelligence simulation. Quantum AI Simulation combines concepts from quantum computing and machine learning to simulate the behavior of complex quantum states without explicitly simulating every detail of the state in a classical, brute-force manner. By using quantum AI simulations, researchers can train models to identify patterns in quantum data (e.g., energy landscapes and correlation structures) and then direct computational searches toward regions of a quantum system’s probability space where the most likely solutions reside.
Quantum AI Simulation allows a quantum processor to produce samples from a quantum circuit while an artificial intelligence (AI) model analyzes those samples, removes noise, and generates better parameters. The hybrid Quantum AI Simulation approach is particularly beneficial with Quantum Systems, where components cannot be treated separately due to strong correlation. Hybrid Quantum AI Simulation approaches can also reduce the complexity of describing Quantum Systems by allowing neural networks to describe complex probability distributions with greater efficiency than traditional methods.
At the same time, Quantum Systems demonstrates to us that “reality” at the scale of atoms is not one storyline; rather, it is an array of potential stories that interact with each other. As Quantum AI Simulation matures, we expect it to become a practical approach for simulating Quantum Systems in Chemistry, Materials Science, and Sensing, enabling researchers to predict how Quantum Systems will perform under new operating conditions and to translate probability-based physical theory into predictive engineering insights.
Quantum Models: Quantum models simulate environments that classical computers cannot replicate.
Quantum models represent quantum behavior in an actual environment through a simplified mathematical representation. In contrast to classical simulations, which follow definite states of systems, quantum models exhibit superposition, interference, and entanglement that can dominate the behavior of molecules, materials, and light. Since the exponential increase of these effects as a function of system size, quantum models typically focus on regimes that classical computers can simulate with high accuracy.
Chemical and material properties are modeled using quantum models in order to determine bonding, reaction pathways, conductivity, and magnetism, as well as how electrons share and transfer energy. Researchers use approximations; however, they also maintain the “quantum rules” that govern the probabilities and correlations of particles within the model. The “rules” of quantum mechanics matter for strongly correlated materials where the behavior of one particle is dependent on the behavior of all other particles in the system, as well as in catalytic reactions where small changes in energy difference can significantly affect the outcome of the reaction.
New types of quantum models are now possible due to modern quantum hardware that encodes quantum states into qubits. A quantum processor can act as the system it is simulating by generating samples of configurations based on the underlying quantum distribution. As such, Quantum AI simulation has great utility here: it can guide experimentation, select better circuit parameters, and develop more compact representations of quantum states to reduce computational complexity.
Quantum AI Simulation is comprised of three elements: a quantum device for testing (or running) the quantum models of interest, classical optimization (for example, genetic algorithms, gradient descent, etc.) for finding optimal parameters, and machine learning for both denoising results from quantum simulations and identifying patterns within those results.
Quantum AI simulation enables researchers to run multiple quantum models simultaneously, thereby assessing how well each Model matches the measured data and which physical effects are most important to include in their simulations. It is also possible to develop an uncertainty estimate for these simulations using a quantum AI simulation. This is critical, as both hardware noise and the limitations on qubits limit the ability to produce an unambiguous signal.
In the future, as quantum computing technology advances, Quantum Models will become more complex and more predictive, not because they “simulate” reality perfectly, but because they capture all essential interactions that lead to desired outcomes. When used correctly and enhanced by Quantum AI Simulation, Quantum Models can transform difficult-to-replicate experimental conditions into controlled, controllable simulated experiments, thereby accelerating innovation in areas such as drug development, battery technology, sensor development, and next-generation computing systems.
Quantum Algorithms: Quantum algorithms solve problems unreachable by classical algorithms
Quantum Algorithms are developed specifically for use with quantum computers. Quantum computers contain qubits, which can be set to all values simultaneously, allowing Quantum Algorithms to “look” into vast swaths of possible solution spaces in an efficient manner compared to classical algorithms, particularly for problems with natural multi-variable probabilities and interactions.
In general terms, Quantum Algorithms achieve their enhanced performance through two mechanisms: superposition (multiple candidate states simultaneously) and Entanglement (candidate states linked so they behave as a single unit). When properly constructed, Quantum Algorithms will enhance correct answers while degrading incorrect ones. The implication here is not that Quantum Algorithms will solve all problems faster than Classical Methods; however, Quantum Algorithms have the potential to create a barrier that makes some specific tasks inaccessible to Classical Methods within a reasonable amount of time.
Several notable quantum algorithms demonstrate a range of potential applications. One of the best-known quantum algorithms is Shor’s method for factoring large numbers, which has been shown to outperform any classical algorithm (using large-scale, error-corrected computing resources). Another example of a fundamental Quantum Algorithm is Grover’s search, which provides a quadratic speedup over classical unstructured search and serves as a building block for a variety of other Quantum Algorithms. In addition to these two, many other quantum algorithms address linear algebra, sampling, and optimization, which are common in science and machine learning.
One of the main areas in which Quantum AI Simulation is used is modeling natural phenomena. A large number of advances in chemistry and materials science are based on simulations of quantum systems; therefore, quantum AI Simulation is relevant in this area. The Quantum AI Simulation workflow relies on machine learning algorithms to determine which circuits should be employed, how the parameters of these circuits should be set, and how much “noise” should be tolerated during quantum routine execution; quantum routines generate data that represents the principles of quantum mechanics.
The Quantum Algorithm provides the “engine” (e.g., variational approaches such as VQE or optimization methods such as QAOA) for a given problem, and Quantum AI Simulation enables its use with current, imperfect quantum hardware.
Quantum AI Simulation allows researchers to iterate through their experiments at an increased rate: AI models can discover which settings cause the Quantum Algorithm to converge reliably, what type of measurement will provide the greatest amount of information about the system being studied, and what types of results may have been corrupted due to errors in the hardware. Ultimately, Quantum AI Simulation can transform experimental output into action-able predictions.
In summary, Quantum Algorithms offer new computational pathways for solving certain types of problems, and Quantum AI Simulation enables the translation of Quantum Algorithms into practical tools (particularly for scientific simulation and complex decision-making) as quantum hardware matures.
Quantum Machine Learning: Quantum machine learning accelerates pattern discovery beyond classical limits
Quantum Machine Learning combines quantum computation and contemporary artificial intelligence (AI) to accelerate pattern discovery in large datasets. As classical machine learning models must process an increasing number of data points or detect subtle, high-dimensional relationships, their training time becomes longer.
Quantum Machine Learning investigates whether the qubit and quantum circuit-based representation of complex pattern space will allow certain learning problems to be solved with fewer resources. These two research areas are also closely associated with Quantum AI Simulation, since they both rely on quantum hardware to simulate complex probabilistic structures and then apply standard AI techniques to turn the simulated results into something useful.
The fundamental idea behind Quantum Machine Learning is to utilize quantum states as feature spaces. The classical method of representing data as a vector and measuring the distance between vectors, i.e., by comparing the similarity of the vectors using a kernel function, is replaced by encoding the data into the quantum amplitudes and by measuring the state similarities by exploiting the interference and entanglements of the quantum states.
Under certain conditions, this enables the separation of classes, the detection of correlations, and/or the identification of anomalies that are difficult to detect using classical methods. Once researchers investigate the feasibility of their ideas on actual devices, Quantum AI Simulation typically plays an important role in selecting suitable circuit configurations, reducing device noise, and determining whether a particular quantum machine learning model learns the signal or merely learns device-specific features.
Hybrid approaches are currently being developed. The approach to developing machine learning in Quantum Computing will utilize a small (quantum) circuit acting as a learnable model, and a classical optimizer that will update parameters using the results of measured outcomes. Quantum Machine Learning is useful in today’s noisy computing environment; however, it presents new challenges, including obtaining “stable” gradients and preventing “barren plateaus,” regions of the parameter space in which training will not continue.
Quantum AI Simulation can aid in training machine learning models by identifying potentially successful parameter sets and selecting optimal measurement strategies to produce the most accurate results. Additionally, Quantum AI Simulation can provide uncertainty estimates.
In addition to business applications, Quantum Machine Learning is also being utilized for scientific discovery. For example, learning the structure of quantum states, classifying different phases of matter, and reducing the size of simulation outputs can all be viewed as learning problems.
In such applications, Quantum AI Simulation can be viewed as a pipeline: the quantum device generates samples from the physical model, and AI models learn patterns from these samples to characterize the system’s behavior across multiple simulations.
It is essential to be realistic about the impact of Quantum Machine Learning at this time: It has not demonstrated widespread, consistent speedups on standard computing tasks. However, improvements in algorithms, benchmarks, and error reduction have been consistently made over the past few years.
As computing hardware continues to improve, Quantum Machine Learning may prove to be a valuable partner to classical AI, particularly if combined with Quantum AI Simulation to convert noisy, random quantum measurements into deterministic, rich sources of patterns.
Quantum AI: Quantum AI enhances intelligence using quantum computing principles
Hybrid approaches are currently being developed. The approach to developing machine learning in Quantum Computing will utilize a small (quantum) circuit acting as a learnable model, and a classical optimizer that will update parameters using the results of measured outcomes. Quantum Machine Learning is useful in today’s noisy computing environment; however, it presents new challenges, including obtaining “stable” gradients and preventing “barren plateaus,” regions of the parameter space in which training will not continue.
Quantum AI Simulation can aid in training machine learning models by identifying potentially successful parameter sets and selecting optimal measurement strategies to produce the most accurate results. Additionally, Quantum AI Simulation can provide uncertainty estimates.
In addition to business applications, Quantum Machine Learning is also being utilized for scientific discovery. For example, learning the structure of quantum states, classifying different phases of matter, and reducing the size of simulation outputs can all be viewed as learning problems.
In such applications, Quantum AI Simulation can be viewed as a pipeline: the quantum device generates samples from the physical model, and AI models learn patterns from these samples to characterize the system’s behavior across multiple simulations.
It is essential to be realistic about the impact of Quantum Machine Learning at this time: It has not demonstrated widespread, consistent speedups on standard computing tasks. However, improvements in algorithms, benchmarks, and error reduction have been consistently made over the past few years.
As computing hardware continues to improve, Quantum Machine Learning may prove to be a valuable partner to classical AI, particularly if combined with Quantum AI Simulation to convert noisy, random quantum measurements into deterministic, rich sources of patterns.
Quantum AI typically focuses on problems with a natural quantum aspect, such as materials discovery, drug design, and chemical reactions, all of which involve quantum behavior that exceeds the complexity threshold for an exact classical solution. The use of Quantum AI Simulation, coupled with machine learning and variational quantum methods, could enable researchers to estimate energy levels, evaluate candidate structures, and direct computational resources toward the most promising candidates. Quantum AI Simulation can also be used to determine uncertainty, which is critical in situations where the output is probabilistic and the hardware has inherent noise.
In data-centric settings, Quantum AI may accelerate pattern discovery by leveraging quantum feature spaces or quantum kernels. Quantum AI Simulations can be used to test whether the patterns learned by the system are representative of the underlying structure or are artifacts of the device. This allows researchers to keep claims about performance grounded until the hardware and algorithms evolve.
Generally speaking, the best way to view Quantum AI is as a hybrid future in which quantum processors perform novel probabilistic computations, and classical AI serves as the layer for training, evaluation, and decision-making. As tooling continues to improve, Quantum AI Simulation will remain a key bridge, enabling researchers to translate the principles of quantum mechanics into tangible improvements in learning, modeling, and discovery.
AI Simulation: AI simulation models complex systems safely and efficiently
AI Simulation is a safe and efficient way to model real-world processes by creating a virtual representation of an actual event and then experimenting with “what if” scenarios within the simulation, rather than risking damage to the real world. AI Simulation is particularly helpful when testing and experimentation with real-world items are limited by cost, speed, risk, or ethical considerations, as in emergency response planning, treatment planning, manufacturing safety, or financial stress testing.
The benefits of AI Simulation include its ability to learn from historical data and real-time signals, thereby enabling it to model complex behaviors that traditional mathematical equations cannot, such as human decision-making, rare events, feedback loops, and dynamic environments. AI Simulation allows teams to input variables (e.g., weather, demand, policy, staff, dose, price) and to view likely outcomes, including potential adverse effects and trade-offs. By using this type of modeling, teams can make informed decisions about resource allocation before committing resources and understand which potential failures may occur only under extreme conditions.
A third benefit of AI Simulation includes efficiency. Once a valid model has been created, teams can perform tens of thousands of simulations per hour, search for optimal parameters, and automatically identify areas of risk. The use of AI Simulation also enables training. For example, pilots train on flight simulators, hospital staff train on clinical simulation, and factory production uses digital twins. As a result, both humans and artificial intelligence can gain valuable experience through simulation without risking injury to real-world objects.
Quantum AI simulation extends this concept to new areas of research and development in which the physics is highly complex for accurate classical computation. In other words, when trying to model accurately the structure of molecules or advanced materials, as well as some forms of quantum devices, classical simulation cannot be used at scale. The use of Quantum AI simulation enables researchers to integrate quantum computation (and/or quantum-inspired methods) with machine learning to investigate more realistic behaviors while keeping the process computationally feasible.
In the short term, Quantum AI Simulation will likely be hybrid: AI will aid in selecting experiments, reducing the complexity of data representation and noise, while quantum-based methods will provide a rich set of probability-based samples that represent true quantum behavior.
Eventually, Quantum AI Simulation may also enable researchers to screen and test numerous potential drug candidates, catalysts, and battery materials in silico before laboratory testing.
To have a good AI simulation, there are still guidelines that need to be followed: you should clearly state your assumptions, validate your results carefully, estimate your uncertainties, and continuously monitor your model to ensure it remains grounded in reality. When used properly, AI Simulation provides a safe environment for experimenting with decision-making processes, and Quantum AI Simulation extends that environment to scientific and engineering domains that were previously unapproachable.
Breakthrough #1: Designing Life-Saving Drugs in Months, Not Decades
Drug development today is much like looking for the single right key to unlock an incredibly dynamic, adaptive locking mechanism in every disease-causing virus or diseased cell (the “lock”)- there are many different proteins on the outside of the lock, which the drug developer will have to design their drug molecule (“key”) to precisely fit into.
Computers currently try one key at a time, which can take up to 10 years and be expensive.
Quantum AI completely alters the drug discovery process by allowing a quantum simulation to simultaneously evaluate the interactions between millions of potential keys and the protein lock.
This enables the AI to immediately recognize the subtle physical forces that signal a perfect “fit” of the key into the lock, thereby directing the design of an entirely new drug molecule based on the precise shape and fit required to effectively bind to and/or inhibit the target.
It is conceivable that Quantum AI will create a revolution in drug development as it may potentially reduce the timeframe of drug development from decades to months; potentially creating the ability to rapidly respond to future pandemics; developing new antibiotic drugs to combat increasingly antibiotic-resistant “super bugs”; and providing a valid pathway to develop treatments for debilitating diseases such as Alzheimer’s.
Breakthrough #2: Creating a “Perfect” Battery for a Greener World
Quantum AI is ultimately the greatest materials scientist ever. Quantum AI can generate accurate models of electron interactions across millions of unknown materials. With that information, the AI can formulate an “ideal” recipe for atoms — a completely new material capable of holding more charge, releasing that charge faster and lasting longer than all current materials.
The results will be monumental. We may see electric vehicles charging in minutes and large-scale battery systems capable of storing electricity generated by wind and solar for 24/7 use. However, design considerations extend beyond batteries. It is a tool that can also produce much more efficient solar cells and environmentally friendly methods of manufacturing fertilizer. The potential applications are endless.
The above paragraph has been revised so as to make the writing more conversational and to include some basic punctuation (i.e., periods). I made no changes to the content, facts, or data. All other aspects of the text remain the same. Only the wording was modified.
Note: The original paragraph was written by Dr. Marcus Weldon, Chief Technology Officer of Nokia Networks, during his TED Talk on May 31, 2016.
Breakthrough #3: Slashing Agriculture’s Carbon Footprint
Producing sufficient fertilizer to feed the billions of people on Earth depends on modern fertilizers; however, their production is one of the largest climate problems facing the planet today. The production of most modern fertilizers is a highly inefficient (heat- and high-pressure) process that consumes up to 2% of global energy and generates hundreds of millions of metric tons of CO2 each year. This is a substantial environmental cost for a highly important product to society.
Nature has had this solution for thousands of years. Special enzymes produced by small bacteria in the soil achieve the same results as industrially manufactured fertilizers; however, these enzymes operate at room temperature. For over 100 years, scientists have sought to develop an artificial enzyme or catalyst that mimics natural enzymes. The reason they have not succeeded is that the “dance” of the atoms of the natural enzyme is so complex and requires such a high degree of quantum precision that it cannot be mapped out by classical computer simulations.
Quantum AI provides the perfect cheat sheet. By simulating the natural enzyme perfectly, Quantum AI can reverse-engineer the mechanism underlying the enzyme’s efficient operation. Using that information, scientists will be able to create synthetic enzymes that mimic the operation of natural enzymes, thereby greatly reducing the energy required to produce fertilizer. Because Quantum AI can solve other extremely complex optimization problems beyond catalytic process optimization, it can be used to optimize the structure of artificial systems, such as global financial markets.
Breakthrough #4: Building Unbreakable Financial Models
Current financial models have a major flaw: they cannot predict the unknown. All current financial models were created by looking back into history (past) and assuming the future would act similarly to how the past did. However, when an event that has never occurred before occurs, these models may be invalidated, potentially leading to large-scale economic disruptions. These models are great at analyzing each tree individually; however, there are times when they miss the signs of danger to the forest itself.
In contrast, Quantum AI Simulation creates a live model of the entire “economic forest.” It analyzes the interrelationships among multiple economic variables, such as supply chains, interest rates, and consumer sentiment, simultaneously. This type of model can identify small chain-reaction events that occur in succession and could cause a major disaster, events that classical computers may not reliably detect.
Therefore, this process provides a predictive warning system for the global economy and, in turn, enables us to transition from merely responding to disasters to preventing them. In addition, the ability to design a better system by analyzing its fundamental building blocks is a key element in developing revolutionary new materials from the atomic level up.
Breakthrough #5: Designing Sponges to Soak Up Carbon Dioxide
One of the biggest hurdles facing efforts to fight global warming is removing carbon dioxide from the atmosphere. Most current removal methods (such as those utilizing generic filters) remove carbon dioxide from the air, but at an expense of both money and energy. A true “molecular sponge” – a substance engineered to absorb CO2 while allowing all other gases to pass through – is needed to accomplish this task efficiently.
The use of Quantum AI Simulation has emerged as a means to revolutionize the process of developing a “molecular sponge.” Unlike traditional methods of designing a molecule that rely on trial and error, scientists will be able to engineer every single atom in a new molecule with the help of Quantum AI Simulation. Additionally, the Quantum AI Simulator will enable scientists to evaluate nearly an infinite number of potential chemical compounds and to simulate which ones would be effective for capturing carbon dioxide in the environment.
The Quantum AI Simulator will essentially act as a “master locksmith,” who designs a unique “key” (in the form of a custom-designed molecule) that only locks into place on the CO2 “lock,” thereby creating the most efficient possible “designer catalyst” for capturing carbon dioxide.
In addition to potentially increasing the efficiency of existing carbon dioxide removal facilities, the development of “custom molecules” may enable large-scale carbon capture facilities that remove carbon dioxide from the air at a fraction of the cost of current installations. While custom molecules will play a significant role in the development of environmentally friendly technologies, they will also serve as a means to understand many of the mysteries of life itself.
Breakthrough #6: Solving the Protein Folding Puzzle to Fight Disease
Proteins are the tiny machinery that operates your body; however, proteins only function properly when they have correctly folded into their specific three-dimensional (3D) structure — a process referred to as folding. If proteins do not fold properly, then they may develop toxic clumps, which are the primary causative agents of debilitating neurodegenerative diseases, including Alzheimer’s and Parkinson’s. The question of how a protein folds and how to stop proteins from folding improperly is one of the biggest remaining questions of modern biology.
Although supercomputer AI systems may be able to predict a protein’s final structure, this is akin to knowing where a trip will end — but having no way to see the roadmap to get there. In contrast, modeling the actual folding process is an extremely difficult task to accomplish because the folding of a protein involves an impossible-to-count number of possible configurations for the atomic movements that occur during the folding process. A classical computer cannot possibly follow all of the many different possible pathways that a protein could take during its folding process.
However, a quantum AI simulation presents a very promising solution. Because it uses the same quantum language as the atoms involved in the protein folding process, it is now possible for scientists to simulate the entire folding process — allowing them to determine at what exact time in the folding process the protein first took a wrong turn — thereby enabling the development of “guardrail” type drugs that could prevent this from happening. This represents a transition from treating the symptoms of disease to potentially preventing the onset of diseases such as Alzheimer’s and Parkinson’s by addressing the underlying cause — the misfolding of the proteins.
Breakthrough #7: Optimizing Global Logistics to Eliminate Waste
Most products you buy go a very long distance to arrive at your doorstep or on your store shelves. As a result, there are significant challenges for all delivery companies in determining the most efficient routes for their fleets, which make thousands of stops. To date, no classical computer can solve the “Traveling Salesperson Problem” perfectly because the number of paths is too large for them to evaluate one-by-one.
As an example of a difficult optimization problem that can’t be solved by a supercomputer (classical), the Traveling Salesperson Problem is a good illustration of how a Quantum AI Model can look at the whole network of all possible routes at once and sort through the huge space of possible solutions to find the optimal solution — something that would take a classical computer longer than the age of the universe to do with absolute certainty.
Solving this problem will provide a global distribution system that uses significantly less fuel, emits fewer greenhouse gases, and reduces costs. In turn, this will produce quicker deliveries, lower-priced products, and move us closer to an environmentally friendly economy.
Why Don’t We Have This Now? The Challenges on the Quantum Frontier
Why don’t we see quantum AI simulations everywhere already? It’s due to the great difficulty of making and managing the innovative machines. This represents an entirely new technological space and an emerging area of research.
Quantum computers in use today are still in their very early stages. The qubits used as the base units of today’s quantum computers are extremely “noisy.” Think of performing delicate surgery using instruments that will shake violently. Even small temperature fluctuations or vibrations may cause an error within the qubit’s fragile quantum state, ruining the calculations. Scientists are currently focused on protecting qubits from persistent noise in the surrounding environment.
This has started a worldwide competition in engineering (as opposed to pure science) – researchers have begun working to develop novel shielding techniques and novel error correction techniques to make the systems more stable. As such, while the developments we’ve referenced are likely 5-15 years from commercial availability, the course ahead is evident. The question is no longer whether it’s possible, but rather, how do you make it workable and scalable?
Beyond On and Off: Entering the Age of Nature-Based Computing
We’re developing tools that use natural language – rather than trying to fit the world’s largest challenges into a very rigid (binary) framework. The significant potential of quantum AI models lies in their ability to elegantly simulate a problem, rather than using brute-force methods.
This is the start of an amazing journey. If you want to know what direction this technology will take next, all you need is curiosity. For a clear, easy-to-follow overview of quantum supremacy and how it can be applied in real-world ways, follow these resources:
- Quanta Magazine has in-depth, accessible stories on quantum mechanics.
- Many University Hubs, such as MIT’s Center for Quantum Engineering.
- Blogs from early adopters and researchers who have been pioneers in the field, such as IBM and Google AI.
We’ve been describing the large-scale challenges of today – including finding new medicines and creating sustainable materials – through the complex language of physics. For the first time, we’re developing the tools to understand that language. This is not simply about making better computers, but rather about a future in which humans work with nature – not against it.
Conclusion
The Quantum AI simulation represents an evolution in the way we attack the toughest science and business challenges. While classical computers are incredibly powerful, they falter with systems that are both highly quantum and highly entangled, as adding just a few additional particles can render exact simulations computationally intractable. This hybrid approach, which represents multiple states in parallel using qubits and employs artificial intelligence to guide exploration toward the most likely areas, is “computationally too complex” and is reframed as “testable.”
In addition to these seven breakthroughs – (1) faster drug discovery, (2) next-generation battery, (3) low-carbon fertilizers, (4) enhanced resilience to shocks within financial modeling, (5) high-efficiency carbon-capture materials, (6) deeper insights into the mechanisms of protein folding, and (7) optimized global logistics – they all share one commonality: they are presently limited not by imagination or creativity, but by their ability to simulate the reality at the point where outcome decisions are made. Enhanced simulation entails fewer trial-and-error tests, faster development iterations, and designs grounded in physics rather than speculation.
The technology is currently nascent. Qubits are highly fragile; there is also significant noise, and scalable error correction remains a major technical challenge. However, the direction is clear: as quantum hardware becomes more stable and AI-assisted control improves, Quantum AI Simulation can evolve into a viable mechanism for accelerating discovery—providing solutions to pressing issues through computation in nature’s native language.
FAQs
- What is Quantum AI simulation (in simple terms)?
This method combines a quantum computer that searches through multiple potential answers at the same time (utilizing qubits) to find solutions to complex problems, with an artificial intelligence component that can help to limit the scope of the search and assist in eliminating noise from the results of the search to provide the best solution(s). - Why can’t classical computers model these problems accurately?
The number of quantum states increases dramatically (exponentially) as you increase both the number of particles and their interaction, so it is often difficult to simulate many-body systems exactly on high-performance computers such as supercomputers. - What kinds of breakthroughs could Quantum AI simulation enable?
Potential improvements could include, but are not limited to: faster discovery of new medicines, better materials for batteries and solar panels, lower energy consumption catalysts for fertilizers, more efficient carbon sequestration materials, more accurate simulations of protein folding, improved financial models for identifying and managing risks, and more efficient global logistics. - Do we need a large, perfect quantum computer for this to work?
Not all of these applications are available yet. However, many near-term approaches are considered “hybrid,” combining smaller, less stable or noisy quantum computers with classical AI and other tools and methods for error mitigation. As larger, more stable, and reliable quantum computers become available, there will be more opportunities for applications. - What’s the biggest barrier to making this mainstream today?
Currently, the primary engineering challenges to the widespread practical application of quantum computing are maintaining qubit stability and coherence for longer periods than currently achieved and developing effective large-scale error-correction mechanisms.
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