N. M. Linke

Demonstration of a small programmable quantum computer with atomic qubits

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 per cent. Reconfiguring these gate sequences provides the flexibility to implement a variety of algorithms without altering the hardware. As examples, we implement the Deutsch–Jozsa and Bernstein–Vazirani algorithms with average success rates of 95 and 90 per cent, respectively. We also perform a coherent quantum Fourier transform on five trapped-ion qubits for phase estimation and period finding with average fidelities of 62 and 84 per cent, 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.

Experimental comparison of two quantum computing architectures

Significance Quantum computers are an emerging technology promising to be vastly more powerful at solving certain problems than any conventional computer. These devices are now moving out of the laboratory and becoming generally programmable. This allows identical quantum tasks or algorithms to be implemented on radically different technologies to inform further development and scaling. We run a series of algorithms on the two leading platforms: trapped atomic ions and superconducting circuits. Whereas the superconducting system offers faster gate clock speeds and a solid-state platform, the ion-trap system features superior qubits and reconfigurable connections. The performance of these systems is seen to reflect the topology of connections in the base hardware, supporting the idea that quantum computer applications and hardware should be codesigned. We run a selection of algorithms on two state-of-the-art 5-qubit quantum computers that are based on different technology platforms. One is a publicly accessible superconducting transmon device (www.research.ibm.com/ibm-q) with limited connectivity, and the other is a fully connected trapped-ion system. Even though the two systems have different native quantum interactions, both can be programed in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantum algorithms between different physical systems. We show that quantum algorithms and circuits that use more connectivity clearly benefit from a better-connected system of qubits. Although the quantum systems here are not yet large enough to eclipse classical computers, this experiment exposes critical factors of scaling quantum computers, such as qubit connectivity and gate expressivity. In addition, the results suggest that codesigning particular quantum applications with the hardware itself will be paramount in successfully using quantum computers in the future.

Fault-tolerant quantum error detection

We show the fault-tolerant encoding, measurement, and operation of a logical qubit via quantum error detection. Quantum computers will eventually reach a size at which quantum error correction becomes imperative. Quantum information can be protected from qubit imperfections and flawed control operations by encoding a single logical qubit in multiple physical qubits. This redundancy allows the extraction of error syndromes and the subsequent detection or correction of errors without destroying the logical state itself through direct measurement. We show the encoding and syndrome measurement of a fault-tolerantly prepared logical qubit via an error detection protocol on four physical qubits, represented by trapped atomic ions. This demonstrates the robustness of a logical qubit to imperfections in the very operations used to encode it. The advantage persists in the face of large added error rates and experimental calibration errors.

Robust 2-Qubit Gates in a Linear Ion Crystal Using a Frequency-Modulated Driving Force

In an ion trap quantum computer, collective motional modes are used to entangle two or more qubits in order to execute multiqubit logical gates. Any residual entanglement between the internal and motional states of the ions results in loss of fidelity, especially when there are many spectator ions in the crystal. We propose using a frequency-modulated driving force to minimize such errors. In simulation, we obtained an optimized frequency-modulated 2-qubit gate that can suppress errors to less than 0.01% and is robust against frequency drifts over ±1  kHz. Experimentally, we have obtained a 2-qubit gate fidelity of 98.3(4)%, a state-of-the-art result for 2-qubit gates with five ions.

Complete 3-Qubit Grover search on a programmable quantum computer

The Grover quantum search algorithm is a hallmark application of a quantum computer with a well-known speedup over classical searches of an unsorted database. Here, we report results for a complete three-qubit Grover search algorithm using the scalable quantum computing technology of trapped atomic ions, with better-than-classical performance. Two methods of state marking are used for the oracles: a phase-flip method employed by other experimental demonstrations, and a Boolean method requiring an ancilla qubit that is directly equivalent to the state marking scheme required to perform a classical search. We also report the deterministic implementation of a Toffoli-4 gate, which is used along with Toffoli-3 gates to construct the algorithms; these gates have process fidelities of 70.5% and 89.6%, respectively.Grover’s algorithm provides a quantum speedup when searching through an unsorted database. Here, the authors perform it on 3 qubits using trapped ions, demonstrating two methods for marking the correct result in the algorithm’s oracle and providing data for searches yielding 1 or 2 solutions.

Demonstration of a Bayesian quantum game on an ion-trap quantum computer

We demonstrate a Bayesian quantum game on an ion trap quantum computer with five qubits. The players share an entangled pair of qubits and perform rotations on their qubit as the strategy choice. Two five-qubit circuits are sufficient to run all 16 possible strategy choice sets in a game with four possible strategies. The data are then parsed into player types randomly in order to combine them classically into a Bayesian framework. We exhaustively compute the possible strategies of the game so that the experimental data can be used to solve for the Nash equilibria of the game directly. Then we compare the payoff at the Nash equilibria and location of phase-change-like transitions obtained from the experimental data to the theory, and study how it changes as a function of the amount of entanglement.

Machine learning assisted readout of trapped-ion qubits

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30\% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.

Comparing the architectures of the first programmable quantum computers

Last year saw the first quantum computers realized that can be programmed from a high level user interface to run arbitrary quantum algorithms [1, 2]. While these devices are still small in scale, only comprising a handful of qubits each, they nevertheless allow the implementation of quantum circuits in a way that is basically blind to the underlying hardware itself. This constitutes a new level of development in quantum computer technology for the two leading approaches, trapped atomic ions [1] and superconducting circuits [3]. It also gives us the opportunity to test quantum computers irrespective of their particular physical implementation for the first time. With multiple companies (IBM, Google, Microsoft, as well as several start-ups) aiming for a larger scale device of commercial viability in the near future, benchmarking quantum computers becomes a crucial pursuit and this work is a step in that direction.