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7.2.4 Performance Last, but certainly not least, the platform has to be able to deliver enough performance to not limit the data rate beyond the physical limits imposed by the experimental setup itself. Since the rate at which data can be collected directly impacts the group’s turn-around time, any slowdown due to the software will translate directly into an undesirable overall slowdown of the project’s rate of progress. I will call this requirement the Performance Requirement. 7.3 Approach To address these issues, Matthew Neeley and I developed a platform called LabRAD. RAD is a programming acronym that stands for Rapid Application Development and usually refers to visual programming environments like Delphi. LabRAD is based on a set of core ideas that help attack the above listed requirements. 7.3.1 Modularity The most important concept for the development of LabRAD was the Modularity Requirement. Being able to break the project up into small pieces not only allows for more rapid progress thanks to parallel development of indepen- 128
dent modules, but it also provides some valuable clues for attacking the other requirements. Most modern programming languages already have the ability to modularize source-code, but many of them lack important features to make the provided facilities sufficient in a physicist driven project. The biggest problem is that most languages do not have a way to manage and index the available modules. In most cases the modules are kept somewhere in the file system and are accessed based on their location or filename. In a physics lab, this usually leads to one code repository per group member. Everyone works in their own directories on their own code using their own conventions. If code is to be shared, usually a copy is made from one user to another, effectively branching the development of that module as both users will now make edits to their copies of the code. Duplication of effort is extremely common due to the difficulties involved in reusing code. This is further aggravated by the general lack of documentation. To alleviate these problems, LabRAD takes modularization from the sourcecode level to the “executable” level. Every module in LabRAD is developed as a completely independent program (or script) and the interaction with external functionality happens at runtime. The different modules communicate via a well defined protocol through a central dispatching agent, the LabRAD Manager. When connecting, each module has to identify itself as either a Server Module 129
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UNIVERSITY of CALIFORNIA Santa Barb
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Benchmarking the Superconducting Jo
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viii
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Curriculum Vitæ Markus Ansmann Edu
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99:187006 Lisenfeld, J., Lukashenko
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Abstract Benchmarking the Supercond
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Contents Contents List of Figures L
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4.1.3 Readout Squid Parameters . .
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7.5.5 Grapher . . . . . . . . . . .
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11.5 Analysis and Verification . .
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List of Figures 2.1 Inductor-Capaci
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List of Tables 3.1 Transition Matri
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Chapter 1 Quantum Computation 1.1 M
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for such problems include factoring
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if present encrypted data will rema
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In terms of quantum bits, this mean
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1.2.3 Implications - The EPR Parado
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proposed architecture of quantum bi
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“coherence times”, i.e. the tim
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Chapter 2 Superconducting Josephson
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of the glue-circuitry needed to con
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Figure 2.1: Inductor-Capacitor Osci
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• The circuit needs to be cooled
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Figure 2.2: Josephson Tunnel Juncti
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the trace along the V-axis, gives c
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Figure 2.4: Josephson Qubits: Sligh
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the cosine forms a local minimum al
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Figure 2.5: Example Qubit Coupling
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Readout schemes can further be cate
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states in the qubit’s inductor, t
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at time t. r is not restricted to b
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3.1.2 Effects of a Time Dependent P
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In some cases, it is possible to so
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Figure 3.1: Examples of Numerical S
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• The energy difference between t
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like this: V = ( V (−1, −1), V
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Figure 3.2: Simulation of LC Oscill
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Table 3.1: Transition Matrix Elemen
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with ω mn = Em−En . Multiplying
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α, it can be ignored. Thus, the in
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e solved exactly: A(t + ∆t) = e
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qubits would be simulated using: A(
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This calculation assumes that the s
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Decoherence consists of two parts:
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Note the difference in signs of the
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Chapter 4 Designing the Phase Qubit
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mutual inductance between the qubit
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During the measurement, the | 1 〉
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the right impedance transformation
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Page 107 and 108: Figure 4.3: Squid I/V Traces - a) L
Page 109 and 110: Figure 4.5: Qubit Integrated Circui
Page 111 and 112: The geometry of the qubit junction
Page 113 and 114: squid loop. Thus, this tool can be
Page 115: now, amorphous silicon seems to pro
Page 118 and 119: Figure 5.1: L-Edit Mask Layout Tool
Page 120 and 121: Figure 5.2: Fabrication Building Bl
Page 122 and 123: Figure 5.3: Photolithography and Et
Page 124 and 125: times the removal can be a bit tric
Page 126 and 127: Figure 5.4: Clearing Vias from Nati
Page 128 and 129: 5.6 Junction Layers 5.6.1 Oxidation
Page 130 and 131: top wiring layer to protect all low
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Page 134 and 135: 6.1 Physical Quality Control during
Page 136 and 137: 6.1.3 Atomic Force Microscopy To re
Page 138 and 139: Figure 6.1: 4-Wire Measurement - a)
Page 140 and 141: 6.3 Quantum Measurements at 25 mK 6
Page 142 and 143: seems to be a box machined out of s
Page 144 and 145: Figure 6.2: Dilution Refrigerator W
Page 146 and 147: cessing data. This protects the vol
Page 148 and 149: 6.3.9 Anritsu Microwave Source The
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Page 152 and 153: ment, the scalability requirements,
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Page 164 and 165: 7.3.4 Performance Addressing the Pe
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Page 168 and 169: Table 7.3: LabRAD Type Annotations
Page 170 and 171: listed in Table 7.3. For transmissi
Page 172 and 173: Architecture to manage network conn
Page 174 and 175: Manager. In fact, in our lab, the o
Page 176 and 177: waiting for their completion. The C
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Page 180 and 181: Since the API guarantees that all R
Page 182 and 183: one microwave line for X/Y-rotation
Page 184 and 185: 7.5.3 DC Rack Server The DC Rack Se
Page 186 and 187: data taking on the lab servers and
Page 188 and 189: keys and the ability to set Context
Page 190 and 191: a certain time. 7.5.9 Optimizer Cli
Page 192 and 193: ters read from different sub-direct
Page 194 and 195: efore the execution of the sequence
Page 196 and 197: can achieve very-close-to hardware
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Page 202 and 203: 8.1 Squid I/V Response As explained
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energy landscape (see Chapter 2.2.3
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Figure 8.3: Squid Steps Failure Mod
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At this point, the squid ramp can b
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starts to tunnel to the neighboring
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Figure 8.5: General Bias Sequence -
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Figure 8.7: Spectroscopy - a) Bias
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Figure 8.8: Rabi Oscillation - a) B
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ensemble with respect to each other
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Figure 8.10: T 1 - a) Bias sequence
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Figure 8.11: Ramsey - a) Bias seque
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phase shift into the middle of the
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photon excitation behaves similarly
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is desirable to modularize the cont
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sequence was run. This allows for t
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possible to read out all qubits cor
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simultaneous application of such me
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Figure 9.4: Capacitive Coupling Swa
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Figure 9.6: Capacitive Coupling Pha
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a phase difference of 0 ◦ rather
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Figure 9.7: Fine Spectroscopy of Re
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Figure 9.8: Swapping Photon into Re
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esonator. The latter calibration is
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Figure 9.11: Resonator T 1 - a) Seq
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224
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He does not throw dice” [Einstein
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(independent of which axis it is),
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of E xy is measured by expressing i
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For a measurement of two particles
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10.2.1 Photons The first and most n
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10.2.3 Ion and Photon In the attemp
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238
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11.1 State Preparation Since the ab
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Figure 11.1: Bell State Preparation
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Table 11.1: Entangled State Density
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Figure 11.2: Bell Measurements - a)
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11.2.3 Statistical Analysis For the
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11.3 Calibration The most difficult
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Table 11.4: Sequence Parameters - R
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ally determine an optimal value for
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ages are based on this method, incl
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equires to converge. Since the algo
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Table 11.7: Bell Violation Results
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Table 11.9: Error Budget - Capaciti
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Bell inequality. To make this claim
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11.5.2 Dependence of S on Sequence
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point where the measurement happens
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The second qubit is not driven and
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Figure 11.6: Resonator Coupled Samp
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From the resulting states, the 16 p
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Table 11.14: Error Budget - Resonat
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sponding to a violation by 244.0σ.
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educe energy decay and dephasing an
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John F. Clauser, Michael A. Horne,
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J. Majer, J. M. Chow, J. M. Gambett
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Matthias Steffen, M. Ansmann, Rados
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