Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-model quantum computers, which represent quantum programs as a sequence of quantum gates acting on a finite number of qubits. Amongst this excitement, analog quantum computation has become less prominent, with the expectation that circuit-model quantum computers will eventually be sufficient for emulating analog quantum computation and thus rendering analog quantum computation obsolete. In this work we explore the basic requirements for emulating a specific analog quantum computation in the circuit model: the preparation of a biased superposition of degenerate ground states of an Ising Hamiltonian using an adiabatic evolution. We show that the overhead of emulation is substantial even for this simple problem. This supports using analog quantum computation for solving time-dependent Hamiltonian dynamics in the short term and midterm, assuming analog errors can be made low enough and coherence times long enough to solve problems of practical interest.
Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-model quantum computers, which represent quantum programs as a sequence of quantum gates acting on a finite number of qubits. Amongst this excitement, analog quantum computation has become less prominent, with the expectation that circuit-model quantum computers will eventually be sufficient for emulating analog quantum computation and thus rendering analog quantum computation obsolete. In this work we explore the basic requirements for emulating a specific analog quantum computation in the circuit model: the preparation of a biased superposition of degenerate ground states of an Ising Hamiltonian using an adiabatic evolution. We show that the overhead of emulation is substantial even for this simple problem. This supports using analog quantum computation for solving time-dependent Hamiltonian dynamics in the short term and midterm, assuming analog errors can be made low enough and coherence times long enough to solve problems of practical interest.
Quantum computers are regarded as the future of computing, as they are believed to be capable of solving extremely complex problems that are intractable on conventional digital computers. However, near-term quantum computers are prone to a plethora of noise sources that are difficult to mitigate, possibly limiting their scalability and precluding us from running any useful algorithms. Quantum emulation is an alternative approach that uses classical analog hardware to emulate the properties of superposition and entanglement, thereby mimicking quantum parallelism to attain similar speeds. By contrast, the use of classical digital hardware, such as field programmable gate arrays (FPGA), is less inefficient at emulating a quantum computer, as it does not take advantage of the fundamentally analog nature of quantum states. Consequently, this approach adds an inherent hardware overhead that also prevents scaling. In this work, an energy-efficient quantum emulator based on analog circuits realized in UMC 180-nm CMOS technology is proposed along with the design methodologies for a scalable computing architecture. A six-fold improvement in power consumption was observed over the FPGA-based approach for a ten-qubit emulation of Grover's search algorithm. The proposed emulator is also about 400 times faster than a Ryzen 5600x six-core processor performing a simulation of six-qubit Grover's search algorithm
Analog circuits have emerged as a valuable quantum emulation and simulation platform. Specifically, they have been experimentally shown to excel in emulating coherent state vector dynamics and motifs of quantum circuits, such as the quantum Fourier transform, tensor product superpositions, two-level systems such as Josephson junctions, and nuclear magnetic resonance state dynamics, all on a very large scale integration chip at room temperature (Cressman et al., 2022; Sarpeshkar, 2019a, 2019b, 2019c; Sarpeshkar, 2020). However, the ability to model simple state vectors is insufficient for modeling open quantum systems, i.e., systems with environmental noise. Noisy quantum systems are essential in practical implementations and applications that exploit noise. The density matrix formalism enables us to model such states, including finite reservoir state systems, and all states that can be represented as state vectors. To our knowledge, no one has yet demonstrated the mapping of a density matrix system to classical analog circuit components. We review the procedure for emulating the dynamics of a finite state vector with four essential analog circuit components and extend this procedure to emulate density matrix dynamics. We then simulate these systems as analog circuits in the presence of noise. This protocol opens up exciting possibilities for further research and development in noisy quantum emulation and simulation using analog circuits for arbitrarily large or small systems.
Quantum state preparation by adiabatic evolution is currently rendered ineffective by the long implementation times of the underlying quantum circuits, comparable to the decoherence time of present and near-term quantum devices. These implementation times can be significantly reduced by realizing these circuits with custom gates. Using classical computing, we model the output of a realistic two-qubit processor implementing the adiabatic evolution of a two-spin system by means of custom gates. This modeled output is then compared with the results of quantum simulations solving the same problem on IBM Quantum (IBMQ) systems. When used to emulate the behavior of the IBMQ quantum circuit, our realistic model yields state fidelities ranging from 65% to 85%, similar to the actual performance of a diverse set of IBMQ devices. When we reduced the implementation time by using custom gates, however, the loss of fidelity was reduced by at least a factor of 4, allowing us to accurately extract the energy of the target state. This improvement is enough to render adiabatic evolution useful for quantum state preparation for small systems or as a preconditioner for other state preparation methods.
One of the key applications for the emerging quantum simulators is to emulate the ground state of many-body systems, as it is of great interest in various fields from condensed matter physics to material science. Traditionally, in an analog sense, adiabatic evolution has been proposed to slowly evolve a simple Hamiltonian, initialized in its ground state, to the Hamiltonian of interest such that the final state becomes the desired ground state. Recently, variational methods have also been proposed and realized in quantum simulators for emulating the ground state of many-body systems. Here, we first provide a quantitative comparison between the adiabatic and variational methods with respect to required quantum resources on digital quantum simulators, namely the depth of the circuit and the number of two-qubit quantum gates. Our results show that the variational methods are less demanding with respect to these resources. However, they need to be hybridized with a classical optimization which can converge slowly. Therefore, as the second result of the paper, we provide two different approaches for speeding the convergence of the classical optimizer by taking a good initial guess for the parameters of the variational circuit. We show that these approaches are applicable to a wide range of Hamiltonian and provide significant improvement in the optimization procedure.
The simulation of quantum systems is the most promising candidate for computational advantagesof large scale, fault-tolerant quantum computers over classical computers. Two fundamental prob- lems that appear when simulating quantum systems on quantum computers are the preparation of good input states to the simulation and the problem of decomposing the time evolution operator ???? into primitive gates that are available on the quantum computer. This thesis presents two new algorithms for solving these problems. The first is a time evolution algorithm that approximates the time evolution operator by combining two previously developed techniques. We bound the error of this composite approach and determine analytic conditions on when this can lead to cost savings of the overall simulation. These savings are then verified through extensive numerics. This simulation algorithm can then be used as a subroutine to prepare thermal states, which are typical input states that have a high overlap with the ground state. Our algorithm is inspired by Hamiltonian Monte Carlo, which is a classical sampling algorithm that utilizes Hamiltonian dynamics to sample from arbitrary probability distributions. We bound the cost of our quantum thermal state preparation algorithm in terms of the total amount of time needed to simulate the Hamiltonian of the system in four scenarios: single qubit systems, truncated harmonic oscillators, non-degenerate systems where one knows the eigenvalues, and non-degenerate systems where no eigenvalue knowledge is present. We numerically demonstrate that this algorithm works as guaranteed and can significantly outperform analytic worst-case bounds.
We construct a completely analog framework that emulates universal quantum gates and quantum algorithms. It is based on electronic circuits made of operational amplifiers, resistors and capacitors. In these circuits, input and output lines represent the computational basis states (CBSs) and thus 2n lines are required to represent n qubits. An operation of the circuits is based on classical evolution and interference of complex amplitudes associated with each CBS. The framework can emulate entangled states and is free from decoherence, measurements are classical and do not collapse states. Similar to physical quantum computers, emulated quantum algorithms can be constructed as a sequence of the gates belonging to a universal set (phase shift, Hadamard, controlled-NOT), as a unitary matrix and as combinations of the two. Circuits representing the universal gates have been made and tested. We also have made a matrix-based emulator of a 3-qubit Grover search algorithm. We tested it by searching for one and two particular states with one and two iterations and found that its outputs accurately match predicted values. We anticipate that the emulators can work as sub-components of physical quantum computers. On their own, the emulators can be used for operations that require a few qubits or operations that can be split into independent (and perhaps weakly entangled) blocks of qubits.