My physics teacher used to say that quantum computers are notoriously bad calculators, and then he’ll quote Richard Feynman to emphasize that these machines were conceived with atomic principles in mind; therefore, they should tackle problems at the atomic scale. Nowhere else is this more tangible than in the fields of chemistry and medicine where quantum is already working hard to make a difference. In this chapter, we showcase two amazing real-life experiments that illustrate how the power of quantum computation can make a difference in the real world.
My physics teacher used to say that quantum computers are notoriously bad calculators, and then he’ll quote Richard Feynman to emphasize that these machines were conceived with atomic principles in mind; therefore, they should tackle problems at the atomic scale. Nowhere else is this more tangible than in the fields of chemistry and medicine where quantum is already working hard to make a difference. In this chapter, we showcase two amazing real-life experiments that illustrate how the power of quantum computation can make a difference in the real world.
In principle, it is possible to model any physical system exactly using quantum mechanics; in practice, it quickly becomes infeasible. Recognising this, Richard Feynman suggested that quantum systems be used to model quantum problems [1]. For example, the fundamental problem faced in quantum chemistry is the calculation of molecular properties, which are of practical importance in fields ranging from materials science to biochemistry. Within chemical precision, the total energy of a molecule as well as most other properties, can be calculated by solving the Schrödinger equation. However, the computational resources required to obtain exact solutions on a conventional computer generally increase exponentially with the number of atoms involved [1, 2]. In the late 1990’s an efficient algorithm was proposed to enable a quantum processor to calculate molecular energies using resources that increase only polynomially in the molecular size [2–4]. Despite the many different physical architectures that have been explored experimentally since that time—including ions, atoms, superconducting circuits, and photons—this appealing algorithm has not been demonstrated to date.
Applications in chemistry and material sciencePerhaps the most credible application of quantum computers is to study quantum physics itself.This deepens our understanding of microscopic systems like molecules, atoms, or even sub-atomic particles, ultimately leading to the discovery of new drugs, materials, and chemical production methods.At first sight, there seems to be a significant advantage compared to conventional computers, which struggle to store the complex quantum state of systems with many particles.As far back as 1981, physicist Richard Feynman ended a conference talk with a famous quote, hinting at the opportunities of quantum computing: 1 I'm not happy with all the analyses that go with just the classical theory, because nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical.Since then, scientists have become increasingly adept at accurately controlling quantum systems.Today, universities boast a wide spectrum of analogue quantum experiments that help us understand nature under exotic circumstances.We're now lining up our tools to take these simulations to the next level: studying nature with digital quantum machines.In this chapter, we will assess how quantum computers can impact the fields of chemistry and material science.That makes this chapter more technical, and we'll assume some (very) basic background in chemistry and physics.We discuss the most relevant algorithms, evaluate claims about quantum computing's benefits in the fight against climate change, and analyse why the nitrogenase enzyme receives such widespread attention.
The difficulty of simulating quantum systems, well known to quantum chemists, prompted the idea of quantum computation. One can avoid the steep scaling associated with the exact simulation of increasingly large quantum systems on conventional computers, by mapping the quantum system to another, more controllable one. In this review, we discuss to what extent the ideas in quantum computation, now a well-established field, have been applied to chemical problems. We describe algorithms that achieve significant advantages for the electronic-structure problem, the simulation of chemical dynamics, protein folding, and other tasks. Although theory is still ahead of experiment, we outline recent advances that have led to the first chemical calculations on small quantum information processors.
Perhaps the most credible application of quantum computers is to study quantum physics itself. This deepens our understanding of microscopic systems like molecules, atoms, or even sub-atomic particles, ultimately leading to the discovery of new drugs, materials, and chemical production methods. At first sight, there seems to be a significant advantage compared to conventional computers, which struggle to store the complex quantum state of systems with many particles. As far back as 1981, physicist Richard Feynman ended a conference talk with a famous quote, hinting at the opportunities of quantum computing: 1 I’m not happy with all the analyses that go with just the classical theory, because nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.
This chapter examines the power of quantum computing, as well as the related concepts of quantum cryptography and teleportation. In 1982, the Nobel prize-winning physicist Richard Feynman noticed there was no simple way of simulating quantum physical systems using digital computers. He turned this problem into an opportunity—perhaps a computational device based on quantum mechanics could solve problems more efficiently than more traditional computers. In the decades that followed, computer scientists and physicists, often working together, showed in theory that quantum computers can solve certain problems, such as factoring numbers, much faster. Whether one can actually build large or even medium-scale working quantum computers and determine exactly what these computers can or cannot do still remain significant challenges.
Turning to quantum mechanics … secret, secret, close the doors! we always have had a great deal of difficulty in understanding the world view that quantum mechanics represents. … It has not yet become obvious to me that there's no real problem. I cannot define the real problem, therefore I suspect there's no real problem, but I'm not sure there's no real problem. So that's why I like to investigate things. – Richard Feynman, 1964 The only difference between a probabilistic classical world and the equations of the quantum world is that somehow or other it appears as if the probabilities would have to go negative. – Richard Feynman, in “Simulating Physics with Computers,” 1982 Our first result is the existence of an efficient universal quantum Turning machine in Deutsch's model. … We give the first formal evidence that quantum Turning machines violate the modern complexity theoretic formulation of the Church Turning thesis. We show the existence of a problem relative to an oracle that can be solved in polynomial time on a quantum Turning machine but require super polynomial time on a bounded error probabilistic Turning machine. – E. Bernstein and U. Vazirani, “Quantum Complexity Theory,” 1997 Quantum computing is a new computational model that may be physically realizable and may provide an exponential advantage over “classical” computational models such as probabilistic and deterministic Turing machines.