Programming a quantum computer with quantum instructions
Morten Kjærgaard, Mollie E. Schwartz, Alex Greene, Gabriel Samach, Andreas Bengtsson, Michael F. O’Keeffe, Christopher M. McNally, Jochen Braumüller, David K. Kim, Philip Krantz, Milad Marvian, Alexander J. Melville, Bethany M. Niedzielski, Youngkyu Sung, Roni Winik, Jonilyn Yoder, Danna Rosenberg, Kevin Obenland, Seth Lloyd, Terry P. Orlando, Iman Marvian, Simon Gustavsson, William D. Oliver
The equivalence between the instructions used to define programs and the input data on which the instructions operate is a basic principle of classical computer architectures and programming. Replacing classical data with quantum states enables fundamentally new computational capabilities with scaling advantages for many applications, and numerous models have been proposed for realizing quantum computation. However, within each of these models, the quantum data are transformed by a set of gates that are compiled using solely classical information. Conventional quantum computing models thus break the instruction-data symmetry: classical instructions and quantum data are not directly interchangeable. In this work, we use a density matrix exponentiation protocol to execute quantum instructions on quantum data. In this approach, a fixed sequence of classically-defined gates performs an operation that uniquely depends on an auxiliary quantum instruction state. Our demonstration relies on a 99.7% fidelity controlled-phase gate implemented using two tunable superconducting transmon qubits, which enables an algorithmic fidelity surpassing 90% at circuit depths exceeding 70. The utilization of quantum instructions obviates the need for costly tomographic state reconstruction and recompilation, thereby enabling exponential speedup for a broad range of algorithms, including quantum principal component analysis, the measurement of entanglement spectra, and universal quantum emulation.
The equivalence between the instructions used to define programs and the input data on which the instructions operate is a basic principle of classical computer architectures and programming. Replacing classical data with quantum states enables fundamentally new computational capabilities with scaling advantages for many applications, and numerous models have been proposed for realizing quantum computation. However, within each of these models, the quantum data are transformed by a set of gates that are compiled using solely classical information. Conventional quantum computing models thus break the instruction-data symmetry: classical instructions and quantum data are not directly interchangeable. In this work, we use a density matrix exponentiation protocol to execute quantum instructions on quantum data. In this approach, a fixed sequence of classically-defined gates performs an operation that uniquely depends on an auxiliary quantum instruction state. Our demonstration relies on a 99.7% fidelity controlled-phase gate implemented using two tunable superconducting transmon qubits, which enables an algorithmic fidelity surpassing 90% at circuit depths exceeding 70. The utilization of quantum instructions obviates the need for costly tomographic state reconstruction and recompilation, thereby enabling exponential speedup for a broad range of algorithms, including quantum principal component analysis, the measurement of entanglement spectra, and universal quantum emulation.
A quantum algorithm consists of a sequence of operations and measurements applied to a quantum processor. To date, the instruction set which defines this sequence has been provided by a classical computer and passed via control hardware to the quantum processor. Here, we demonstrate the first experimental realization of a quantum instruction set, in which a fixed sequence of classically-defined gates perform an operation that is fully determined only by a quantum input to the fixed sequence. Specifically, we implement the density matrix exponentiation algorithm, which consumes N copies of the instruction state ρ to approximate the operation e−iρθ (θ an arbitrary angle). Our implementation relies on a 99.7% fidelity controlled-phase gate between two superconducting transmon qubits. We achieve an average algorithmic fidelity ≈0.9, independent of the setting of ρ, to circuit depth nearly 90. This new paradigm for quantum instructions has applications to resource-efficient protocols for validating entanglement spectra, principal component analysis of large quantum states, and universal quantum emulation.
Quantum computers can solve certain problems more efficiently compared to conventional classical methods. In the endeavor to build a quantum computer, several competing platforms have emerged that can implement certain quantum algorithms using a few qubits. However, the demonstrations so far have been done usually by tailoring the hardware to meet the requirements of a particular algorithm implemented for a limited number of instances. Although such proof of principal implementations are important to verify the working of algorithms on a physical system, they further need to have the potential to serve as a general purpose quantum computer allowing the flexibility required for running multiple algorithms and be scaled up to host more qubits. Here we demonstrate a small programmable quantum computer based on five trapped atomic ions each of which serves as a qubit. By optically resolving each ion we can individually address them in order to perform a complete set of single-qubit and fully connected two-qubit quantum gates and alsoperform efficient individual qubit measurements. We implement a computation architecture that accepts an algorithm from a user interface in the form of a standard logic gate sequence and decomposes it into fundamental quantum operations that are native to the hardware using a set of compilation instructions that are defined within the software. These operations are then effected through a pattern of laser pulses that perform coherent rotations on targeted qubits in the chain. The architecture implemented in the experiment therefore gives us unprecedented flexibility in the programming of any quantum algorithm while staying blind to the underlying hardware. As a demonstration we implement the Deutsch-Jozsa and Bernstein-Vazirani algorithms on the five-qubit processor and achieve average success rates of 95 and 90 percent, respectively. We also implement a five-qubit coherent quantum Fourier transform and examine its performance in the period finding and phase estimation protocol. We find fidelities of 84 and 62 percent, respectively. While maintaining the same computation architecture the system can be scaled to more ions using resources that scale favorably (O(N^2)) with the number of qubits N.
This paper proposes a novel approach to computation by leveraging quantum entanglement as a fundamental computational logic, rather than solely as a means of data transmission. We introduce a framework for constructing parallel computation architectures based on entangled states, aiming to achieve exponential speedups compared to classical computation. The core of this approach lies in designing quantum gate circuits that exploit entanglement to perform parallel data processing and logical operations. A significant portion of this work focuses on the critical challenges of maintaining and controlling entanglement over extended periods, which is paramount for the feasibility of this paradigm. We explore theoretical models and design considerations for these entangled gate circuits, examining the inherent parallelism and potential for complex computations. The underlying mathematical framework utilizes concepts from quantum information theory, including qubit states, unitary transformations, and entanglement measures. We define key operational concepts such as entanglement fidelity, concurrence, and partial transposition. The proposed system is inherently probabilistic, and we address the challenges of error mitigation and fault tolerance. This research lays the groundwork for a fundamentally different approach to computation, potentially revolutionizing fields reliant on intensive computation, such as drug discovery, materials science, and artificial intelligence.
Quantum computers can solve certain problems more efficiently than any possible conventional computer. Small quantum algorithms have been demonstrated on multiple quantum computing platforms, many specifically tailored in hardware to implement a particular algorithm or execute a limited number of computational paths. Here, we demonstrate a five-qubit trapped-ion quantum computer that can be programmed in software to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates. We compile algorithms into a fully-connected set of gate operations that are native to the hardware and have a mean fidelity of 98 %. Reconfiguring these gate sequences provides the flexibility to implement a variety of algorithms without altering the hardware. As examples, we implement the Deutsch-Jozsa (DJ) and Bernstein-Vazirani (BV) algorithms with average success rates of 95 % and 90 %, respectively. We also perform a coherent quantum Fourier transform (QFT) on five trappedion qubits for phase estimation and period finding with average fidelities of 62 % and 84 %, respectively. This small quantum computer can be scaled to larger numbers of qubits within a single register, and can be further expanded by connecting several such modules through ion shuttling or photonic quantum channels.
Quantum mechanics has proven its position as the primary theory for accurately describing microscopic domains. Numerous fields in physics utilize quantum mechanics to great effects, with one prominent application being quantum computing. Quantum computing extends the field of quantum mechanics to information theory, striving to perform computational tasks utilizing quantum phenomena with notably greater efficiency compared to classical alternatives. In this thesis, I will go through the basics required in the field of quantum computing. To understand quantum computing demands adapting a new type of matrix mechanical view. This involves considering Hilbert spaces, which are linear vector space having a well defined inner product. Hilbert spaces contain state vectors describing quantum states, as well as operators for altering and measuring these states. The dynamics of quantum systems are governed by three quantum postulates. These specify Hilbert spaces and state vectors as the fundamental components of quantum systems, Hermitian operators as providing the physical manifestation of these systems, and finally, the Schrödinger's equation to describe the time evolution of such systems. These notions are required in producing quantum computing. Quantum computing uses qubits as the computational basis, analogous to classical bits but with the added capability of quantum superposition. Qubits are represented as two-state vectors, and can be physically realized with any quantum-behaving binary-valued system, like the electron spins. Qubits can be combined into higher dimensional states with the use of tensor products, causing the system's Hilbert space to expand as 2^n with n being the number of qubits, allowing for exponential computational speedup. Computational logic can be constructed by acting on the qubits using unitary operators, known as quantum gates. The combinations of qubits and quantum gates can be visualized as quantum circuits, depicted using specialized circuit diagram notations. An essential part of quantum computing, and the phenomena enabling exponential computational speedup, is quantum entanglement. Entanglement arises in interacting qubits, intertwining them so that the individual states become correlated. Entanglement can be characterized by being unable to reduce a combined state into its pure constituents. The amount of entanglement in a system can be measured using von Neumann entropy, analogous to Shannon entropy in classical information theory. Entanglement serves various purposes in quantum computing and is a defining feature distinguishing it from the classical domain. In this thesis, the entanglement properties of a periodic four-qubit circuit with alternating quantum gates were simulated. The simulation was performed computationally, and measured entanglement entropy as a function of the amount of circuit periods. The results demonstrated different characteristics of entanglement depending on the parameters used for the quantum gates, with the entanglement either being periodic, non-occurring or chaotic in nature.
The design and architecture of a quantum instruction set are paramount to the performance of a quantum computer. This work introduces a gate scheme for qubits with XX + YY coupling that directly and efficiently realizes any two-qubit gate up to single-qubit gates. First, this scheme enables high-fidelity execution of quantum operations, especially when decoherence is the primary error source. Second, since the scheme spans the entire SU(4) group of two-qubit gates, we can use it to attain the optimal two-qubit gate count for algorithm implementation. These two advantages in synergy give rise to a quantum Complex yet Reduced Instruction Set Computer (CRISC). Though the gate scheme is compact, it supports a comprehensive array of quantum operations. This may seem paradoxical but is realizable due to the fundamental differences between quantum and classical computer architectures.