Scalable fault-tolerant quantum computation in DFS blocks

We investigate how to concatenate different decoherence-free subspaces (DFSs) to realize scalable universal fault-tolerant quantum computation. Based on tunable $XXZ$ interactions, we present an architecture for scalable quantum computers which can fault-tolerantly perform universal quantum computation by manipulating only single type of parameter. By using the concept of interaction-free subspaces we eliminate the need to tune the couplings between logical qubits, which further reduces the technical difficulties for implementing quantum computation.Comment: 4 papges, 2 figure

Quantum computation with un-tunable couplings

Most quantum computer realizations require the ability to apply local fields and tune the couplings between qubits, in order to realize single bit and two bit gates which are necessary for universal quantum computation. We present a scheme to remove the necessity of switching the couplings between qubits for two bit gates, which are more costly in many cases. Our strategy is to compute in and out of carefully designed interaction free subspaces analogous to decoherence free subspaces, which allows us to effectively turn off and turn on the interactions between the encoded qubits. We give two examples to show how universal quantum computation is realized in our scheme with local manipulations to physical qubits only, for both diagonal and off diagonal interactions.Comment: 5 pages, 2 figure

Design of an RSFQ Control Circuit to Observe MQC on an rf-SQUID

We believe that the best chance to observe macroscopic quantum coherence (MQC) in a rf-SQUID qubit is to use on-chip RSFQ digital circuits for preparing, evolving and reading out the qubit's quantum state. This approach allows experiments to be conducted on a very short time scale (sub-nanosecond) without the use of large bandwidth control lines that would couple environmental degrees of freedom to the qubit thus contributing to its decoherence. In this paper we present our design of a RSFQ digital control circuit for demonstrating MQC in a rf-SQUID. We assess some of the key practical issues in the circuit design including the achievement of the necessary flux bias stability. We present an "active" isolation structure to be used to increase coherence times. The structure decouples the SQUID from external degrees of freedom, and then couples it to the output measurement circuitry when required, all under the active control of RSFQ circuits. Research supported in part by ARO grant # DAAG55-98-1-0367.Comment: 4 pages. More information and publications at http://www.ece.rochester.edu:8080/users/sde/research/publications/index.htm

Economic Outlook for Representative Cotton Farms Given the August 2005 FAPRI/AFPC Baseline

The Agricultural and Food Policy Center (AFPC) at Texas A&M University develops and maintains data to simulate eighteen representative cotton operations in major production areas of seven states. The chief purpose of this analysis is to project those farms’ economic viability for 2005 through 2009. The data necessary to simulate the economic activity of these operations is developed through ongoing cooperation with panels of agricultural producers in each of these states. The Food and Agricultural Policy Research Institute (FAPRI) provided projected prices, policy variables, and input inflation rates in their August 2005 Baseline. Under the August 2005 Baseline, only the moderately sized Tennessee cotton farm (TNC1900) and Louisiana cotton farm (LAC2640) are considered in good liquidity condition (less than a 25 percent chance of negative ending cash during 2005-2009). Five cotton farms (TXSP3745, TXRP2500, TXMC3500, TXCB1850, and TNC4050) have between a 25 percent and a 50 percent likelihood of negative ending cash. The remaining eleven cotton farms have greater than a 50 percent chance of negative ending cash. Additionally, TNC1900 is the only farm in the set considered in good equity position (less than a 25 percent chance of decreasing real net worth during 2005-2009). Three cotton farms (TXRP2500, TXMC3500, and TXCB1850) have between a 25 percent and 50 percent likelihood of losing real net worth, and the remaining fourteen cotton farms have greater than a 50 percent probability of decreasing real net worth.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Economic Outlook for Representative Cotton Farms Given the August 2003 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative cotton farms are projected in this report. The analysis was conducted over the 2001-2007 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major cotton production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative cotton farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2003 Baseline. The primary objective of the analysis is to determine the farms’ economic viability by region through the life of the 2002 Farm Bill, given sector level conditions projected in the August 2003 FAPRI Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by cotton farmers for prices and production. This report presents the results of the August 2003 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2007. This report is organized into five sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI August 2003 Baseline and the policy and price assumptions used for the representative farm analyses. The third section presents the results of the simulation analyses for cotton farms. Two appendices constitute the final section of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative cotton farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,