Recent work has demonstrated the feasibility of using an array of quantum information processors connected via classical channels (Type II Quantum Computer) to calculate the quantum lattice-gas algorithm. This thesis describes the construction and implementation of a new hardware scheme for a Type II Quantum computer. This system has different hardware and software specifications but does follow previously published approaches of operation encoding the initial mass density onto a two-qubit processor and using standard pulse techniques to step through the algorithm. New hardware for this system includes the use of multiple coils controlled by a hardware switch array as well as a full function, two channel spectrometer for dual readouts. New software includes a top-level control system for the spectrometer for quick experimental configuration as well as reconfigurable modeling software to verify results. Results are presented here for the quantum lattice gas algorithm for the diffusion equation run on two systems: a prototype configuration using a single coil in a 1.5 Tesla magnet and a final configuration using a coil array in a 5.0 Tesla magnet.*
*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat.
Recent work has demonstrated the feasibility of using an array of quantum information processors connected via classical channels (Type II Quantum Computer) to calculate the quantum lattice-gas algorithm. This thesis describes the construction and implementation of a new hardware scheme for a Type II Quantum computer. This system has different hardware and software specifications but does follow previously published approaches of operation encoding the initial mass density onto a two-qubit processor and using standard pulse techniques to step through the algorithm. New hardware for this system includes the use of multiple coils controlled by a hardware switch array as well as a full function, two channel spectrometer for dual readouts. New software includes a top-level control system for the spectrometer for quick experimental configuration as well as reconfigurable modeling software to verify results. Results are presented here for the quantum lattice gas algorithm for the diffusion equation run on two systems: a prototype configuration using a single coil in a 1.5 Tesla magnet and a final configuration using a coil array in a 5.0 Tesla magnet.*
*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat.
As proposed, we upgraded the system console on an existing Bruker Instruments, 14 T nuclear magnetic resonance (NMR) spectrometer to enable an improved implementation of type II quantum computers (TTQC). This upgrade is fully functional and has permitted our NMR studies to be moved to higher strength magnetic fields for better sensitivity and spectral dispersion. The TTQC experiments continue in collaboration with Dr. J. Yepez of the Air Force Research Laboratory. We have completed studies of the I-D dynamics of the diffusion equation and the Burger's equation with various viscosity. We have nearly completed additional studies showing both the scaling of errors in TTQC and a new novel mapping to reciprocal space lattices. The latter is hoped to be a first step toward running the TTQC with a closed feedback loop. The closed loop computations will permit continuous quantum computation in a lattice gas architecture.
A lattice-gas algorithm for the one-dimensional diffusion equation is realized using radio frequency pulses in a one-dimensional spin system. The model is a large array of quantum two-qubit nodes interconnected by the nearest-neighbor classical communication channels. We present a quantum protocol for implementation of the quantum collision operator and a method for initialization and reinitialization of quantum states. Numerical simulations of the quantum-classical dynamics are in good agreement with the analytic solution for the diffusion equation.
Standard CMOS circuits are expected to be no longer able to satisfy nowadays demand for higher performances and lower power consumption. This commits to look for new solutions. Quantum computation, whose fundamental quantity of information is the qubit, is a thoroughly new way of thinking to electronics. There are at least two main reasons for which quantum computing is expected to broaden computation horizons. First, specific algorithms can be engineered, manipulating the probability amplitudes of different states, to exploit the quantum superposition principle and achieve high parallelism. Second, it is expected to be spontaneously suitable to simulate complex quantum physical systems, as large molecules. Quantum computing requires the cooperation of several scientific fields. The leading idea has been to highlight the operations and algorithms a quantum computer must be able to perform and, then, to propose a hardware solution, providing the rigorous physical treatment mandatory for a deep understanding and for an appropriate modelling, something which is hard to find in nowadays literature. Thus, the first chapters address the quantum algorithms, focusing on Grover's search algorithm and deriving, from fundamentals, the corresponding circuit. The latter is then described in Quantum Assembly (QASM), which can be seen as the quantum equivalent of VHDL, and simulated on IBM Q Experience. The following chapters deal with a possible physical implementation of quantum processors: the encoding of qubits on nuclear spins in diamagnetic molecules. When a static magnetic field is applied to a spin-1/2 nucleus, two well-defined energy eigenstates arise, as a result of the Zeeman effect. Each of them can be associated with a qubit eigenstate. Thanks to nuclear magnetic resonance principles, the probability with which one of the two eigenstates is measured can be handled by the superposition of a radio frequency field at the resonance frequency of the nucleus. This can be interpreted as a rotation of the spin, and so of the qubit, about the RF field and enables the execution of single qubit quantum gates, since they are simple rotations of the state vector. The two-qubit CNOT gate is obtained through the interaction of spins via J-coupling. Considering that these gates constitute a universal set, every quantum algorithm can theoretically be run on an NMR processor. Different nuclei have different resonant frequencies, but the addressing of a specific spin can be achieved also in homonuclear molecules, since the resonant frequency depends also on the nuclear environment (nuclear shielding). While unwanted J-couplings can be removed thanks to refocusing, spin decoherence and relaxation phenomena force an upper limit to the allowed timescale. A main advantage of NMR is that this timescale is long, even at room temperature. Conversely, scalability is still an issue. A simple heteronuclear MATLAB model, based on the relations derived from first principles in the thesis, is proposed to prove the feasibility of NMR quantum processors. The input technological parameters, as chemical shielding and J-coupling, can be computed resorting on ORCA, or obtained from experimental data. The model, able to run a universal set of quantum gates and simple algorithms, can be useful to find an optimal operating point as a compromise between molecule physical properties and the quantities which can be controlled by nowadays NMR instrumentation.
The development of quantum error correcting techniques is of paramount importance to the ultimate goal of implementing practical quantum computers. The simultaneous transmission of quantum and classical information over a quantum channel was initially investigated by Shor and since been continued by others. It was shown that there is an advantage to transmitting both quantum and classical information simultaneously, compared to independent transmissions. The characterization and construction of codes that allow to transmit both quantum and classical information, which we refer to as hybrid codes, was done from a coding theory perspective and by using the operator algebra quantum error correction (OAQEC) perspective. In this work we unify these two perspectives, showing that the coding theory formulation is a specific case of the OAQEC perspective. As a result we generalized the quantum hamming bound to the hybrid case. To date no such hybrid codes have been physically implemented. Nuclear magnetic resonance (NMR) quantum information processors (QIP) have been an excellent test bed for quantum computing. The techniques of NMR had been refined for many years and can now be applied to quantum computing. NMR techniques provides a high degree of qubit control and long decoherence times compared to other QIP. These two properties of NMR QIP make it a prime candidate for implementing hybrid codes. In this work we developed a hybrid code and designed the circuit for encoding it. We used Matlab to simulate the pulse sequence which would be used in an NMR QIP to carry out the encoding of the hybrid circuit. According to those simulations the full encoding, a single qubit Pauli error and a full decoding can be carried out without the effects of decoherence destroying the quantum information beyond retrieval.
We present two experimental schemes that can be used to implement the Factorized Quantum Lattice-Gas Algorithm for the 1D Diffusion Equation with Persistent-Current Qubits. One scheme involves biasing the PC Qubit at multiple flux bias points throughout the course of the algorithm. An implementation analogous to that done in Nuclear Magnetic Resonance Quantum Computing is also developed. Errors due to a few key approximations utilized are discussed and differences between the PC Qubit and NMR systems are highlighted.
This document aims to describe the methodology of quantum lattice gas algorithm based quantum physics simulations with details on implementation using graphics processing units. A variety of examples illustrating the general application of these methods are included. These examples are used to highlight topics such as scaling, potentials, momentums, multiple particle and dimensions, and various Hamiltonians in quantum simulations. Although intended to fullfill the doctor of philosophy degree requirement, this document is well suited as an introduction to this approach to modeling physics. In recent times parallelization has significantly improved the available computational power. Graphics processing units (GPUs) are a shining example of this, with thousands of compute cores and continually improved general computational capabilities, these processors can outshine their central processing unit (CPU) counterparts by orders of magnitude. Presented here are a number of examples of efficient computational methods for simulating physics using GPUs. Our quantum simulation method involves the quantum lattice gas model of quantum computation [59, 68, 64], however much of the parallel technology implementation is applicable to other numerical approaches. The quantum lattice gas model of computation is based on a unitary stream-collide quantum algorithm applied repeatedly over time upon an ordered array of qubits [53, 56, 68, 61, 63, 67]. The stream-collide quantum algorithm represents local quantum evolution of the system of quantum particles whose motion is constrained to reside on a spacetime lattice. Each particle in the system undergoes a quantum walk, interacting locally with other particles in the system that may reside on the same point. We will first mathematically describe the basic idea of quantum lattice gas algorithms. Next we will discuss GPUs and the basic features needed for implementation. We will then describe several examples of quantum lattice gas algorithm approach to illustrate the various applications and implementation challenges. Of particular interest is a look at modeling time-dependent many-body quantum mechanics using quantum lattice gas methods. For practical purposes we focus on the case of two fermions governed by the nonrelativistic Schrödinger equation. Numerical comparisons to analytic prediction show the high accuracy of these methods. Quantum lattice gas methods are chosen as a unitary algorithm for time dependent quantum simulation as they are efficient, accurate, and avoid the Fermi sign problem. Our findings from two-particle simulations under various conditions include the physics of the one and two particle systems diverging in their respective time-dependent behavior with increasing entanglement. We also confirm the multiple particle extension of the Ehrenfest theorem. Using the examples discussed throughout this document, we will then describe our specific computational implementation utilizing the computational power of GPUs through Nvidia’s compute unified device architecture (CUDA) language. Here we will discuss the open source software our group is developing based on these methods, along with some implementation verification and debugging concepts. Education is another application peripherally connected to this work and our software. The applications of this type of modeling as a learning tool is not fully explored yet, but the opportunities are promising.