In terms of leveraging the total power of quantum computing, the prevalent current (2020) model of designing quantum computation devices to follow the von Neuman model of abstraction is highly unlikely to be making full use of the full range of computational assistance possible at the atomic and molecular level. This is particularly the case for molecular modeling, in using computational models that more directly leverage the quantum effects of one set of molecules to estimate the behavior of some other set of molecules would remove the bottleneck of insisting that modeling first be converted to the virtual binary or digital format of quantum von Neuman machines. It is argued that even though this possibility of “fighting molecular quantum dynamics with molecular quantum dynamics” was recognized by early quantum computing founders such as Yuri Manin and Richard Feynman, the idea was quickly overlooked in favor of the more computer-compatible model that later developed into qubits and qubit processing.
In terms of leveraging the total power of quantum computing, the prevalent current (2020) model of designing quantum computation devices to follow the von Neuman model of abstraction is highly unlikely to be making full use of the full range of computational assistance possible at the atomic and molecular level. This is particularly the case for molecular modeling, in using computational models that more directly leverage the quantum effects of one set of molecules to estimate the behavior of some other set of molecules would remove the bottleneck of insisting that modeling first be converted to the virtual binary or digital format of quantum von Neuman machines. It is argued that even though this possibility of “fighting molecular quantum dynamics with molecular quantum dynamics” was recognized by early quantum computing founders such as Yuri Manin and Richard Feynman, the idea was quickly overlooked in favor of the more computer-compatible model that later developed into qubits and qubit processing.
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.
Our understanding of the physics of biological molecules, such as proteins and DNA, is limited because the approximations we usually apply to model inert materials are not, in general, applicable to soft, chemically inhomogeneous systems. The configurational complexity of biomolecules means the entropic contribution to the free energy is a significant factor in their behaviour, requiring detailed dynamical calculations to fully evaluate. Computer simulations capable of taking all interatomic interactions into account are therefore vital. However, even with the best current supercomputing facilities, we are unable to capture enough of the most interesting aspects of their behaviour to properly understand how they work. This limits our ability to design new molecules, to treat diseases, for example. Progress in biomolecular simulation depends crucially on increasing the computing power available. Faster classical computers are in the pipeline, but these provide only incremental improvements. Quantum computing offers the possibility of performing huge numbers of calculations in parallel, when it becomes available. We discuss the current open questions in biomolecular simulation, how these might be addressed using quantum computation and speculate on the future importance of quantum-assisted biomolecular modelling.
Our understanding of the physics of biological molecules, such as proteins and DNA, is limited because the approximations we usually apply to model inert materials are not, in general, applicable to soft, chemically inhomogeneous systems. The configurational complexity of biomolecules means the entropic contribution to the free energy is a significant factor in their behaviour, requiring detailed dynamical calculations to fully evaluate. Computer simulations capable of taking all interatomic interactions into account are therefore vital. However, even with the best current supercomputing facilities, we are unable to capture enough of the most interesting aspects of their behaviour to properly understand how they work. This limits our ability to design new molecules, to treat diseases, for example. Progress in biomolecular simulation depends crucially on increasing the computing power available. Faster classical computers are in the pipeline, but these provide only incremental improvements. Quantum computing offers the possibility of performing huge numbers of calculations in parallel, when it becomes available. We discuss the current open questions in biomolecular simulation, how these might be addressed using quantum computation and speculate on the future importance of quantum-assisted biomolecular modelling.
Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route to solving some types of computational problems, including several of relevance to biological simulations. In particular, optimization problems, both convex and non-convex, feature in many biological models, including protein folding and molecular dynamics. Early quantum computers will be small, reminiscent of the early days of digital silicon computing. Understanding how to exploit the first generation of quantum hardware is crucial for making progress in both biological simulation and the development of the next generations of quantum computers. This review outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.
Molecules are serious candidates for building hardware for quantum computers. They can encode quantum information onto electron or nuclear spins and some of them show important features as the scalability of the number of qubits and a universal set of quantum gates. In this paper we present our advances in the development of a classical simulation infrastructure for molecular Quantum Computing: starting from the definition of simplified models taking into account the main physical features of each analyzed molecule, quantum gates are defined over these models, thus permitting to take into account the real behavior of each technology during the simulation. An interface with a hardware-agnostic description language has been also developed. The knowledge of the behavior of real systems permits to optimize the design of quantum circuits at both physical and compilation levels. Elementary quantum algorithms have been simulated on three different molecular technologies by changing the physical parameters of polarization and manipulation and quantum circuit design strategies. Results confirm the dependency of the fidelity of the results on both levels, thus proving that the choice of optimal operating points and circuit optimization techniques as virtual-Z gates are fundamental for ensuring the execution of quantum circuits with negligible errors.
Traditional computing uses transistors and binary logic in order to perform computing operations. This approach has proved successful as long as the number of transistors per integrated circuit can be scaled to accommodate increasing speed and heat requirements. Unfortunately, this approach does not allow speeds above the limit where the size of the transistors approaches the limit of the integrated circuit substrates' molecular size. In this condition, electrons cannot reliably be contained within the boundaries separating one transistor from the next and increasingly small circuits become impractical. The current solution to this dilemma is to increase the number of functional units on an integrated circuit and thereby eliminate the need for increasingly small transistors. However whether we increase the number of functional units or the speed, there are inherent limitations in the number of transistors we can put onto one chip. We therefore look to another functional approach to generate the next generation of integrated circuits. We therefore look to using the molecules and elementary particles themselves as a means of computation. In order to accomplish molecular computation we need to modify the model of traditional computation from a transistor based binary method to analog based arbitrary basis model. This approach is not unknown since optical computation among other has used analog processing for highly parallel computation. The issue with molecular and quantum computing methods has traditionally been that understanding the state the computing function itself is often subject to high degree uncertainty. This uncertainty is not unexpected since the number of possible states that can exist when electromagnetic energy interrogates a collection of molecules, atoms, or electrons is quite large. In order to develop a method of representing these states we use a partially observable Markov decision process. We will then develop a finite state machine approach to computation based on a model for our molecular or quantum system.